Categorizing Objects for Queries on Online Social Networks

ABSTRACT

In one embodiment, a method includes receiving a query inputted by the user; identifying a set of objects matching the query; calculating, for each identified object, a plurality of category-scores corresponding to a plurality of categories, respectively, wherein each category-score is calculated based on a plurality of sub-scores corresponding to a plurality of scoring axes; categorizing each identified object into a category of the plurality of categories based on the category-scores for the identified object; and sending, to the client system in response to the query, one or more search results corresponding to one or more of the categorized objects for display, each search result referencing the respective categorized object, wherein the one or more categorized objects of the search results comprises objects categorized into one or more selected categories.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 15/260214, filed 8 Sep. 2016, which is incorporatedherein by reference.

TECHNICAL FIELD

This disclosure generally relates to social graphs and performingsearches for objects within a social-networking environment.

BACKGROUND

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. wall posts,photo-sharing, event organization, messaging, games, or advertisements)to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

Social-graph analysis views social relationships in terms of networktheory consisting of nodes and edges. Nodes represent the individualactors within the networks, and edges represent the relationshipsbetween the actors. The resulting graph-based structures are often verycomplex. There can be many types of nodes and many types of edges forconnecting nodes. In its simplest form, a social graph is a map of allof the relevant edges between all the nodes being studied.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the social-networking system may send to theclient system of a user categorized search results in response to aquery received from the client system. The social-networking system mayidentify a set of objects that match a query and calculate, for eachobject, category-scores corresponding to a plurality of categories(e.g., a news category, a celebrity category, a commentary category,etc.). Each category-score may be calculated based on a plurality ofsub-scores corresponding to a plurality of scoring axes, respectively,each scoring axis corresponding to a particular factor (e.g., recency,textual similarity, author quality, etc.). Each category-score may be aweighted arithmetic mean of the sub-scores, where the weighting is basedon the category corresponding to the category score. The identifiedobjects may be categorized into one of the plurality of categories basedon its category-scores, and the social-networking system may send searchresults referencing the categorized objects to the client system.

The embodiments disclosed above are only examples, and the scope of thisdisclosure is not limited to them. Particular embodiments may includeall, some, or none of the components, elements, features, functions,operations, or steps of the embodiments disclosed above. Embodimentsaccording to the invention are in particular disclosed in the attachedclaims directed to a method, a storage medium, a system and a computerprogram product, wherein any feature mentioned in one claim category,e.g. method, can be claimed in another claim category, e.g. system, aswell. The dependencies or references back in the attached claims arechosen for formal reasons only. However any subject matter resultingfrom a deliberate reference back to any previous claims (in particularmultiple dependencies) can be claimed as well, so that any combinationof claims and the features thereof are disclosed and can be claimedregardless of the dependencies chosen in the attached claims. Thesubject-matter which can be claimed comprises not only the combinationsof features as set out in the attached claims but also any othercombination of features in the claims, wherein each feature mentioned inthe claims can be combined with any other feature or combination ofother features in the claims. Furthermore, any of the embodiments andfeatures described or depicted herein can be claimed in a separate claimand/or in any combination with any embodiment or feature described ordepicted herein or with any of the features of the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with asocial-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example partitioning for storing objects of asocial-networking system.

FIG. 4A illustrates an example set of weighted sub-scores correspondingto an example set of categories for an example identified object.

FIG. 4B illustrates an example set of particular weighted sub-scorescorresponding to an example set of particular categories for an exampleidentified object.

FIG. 5A illustrates an example set of category scores for an example setof identified objects.

FIG. 5B illustrates an example set of particular category-scorescorresponding to an example set of identified objects.

FIG. 6 illustrates an example post on a social network and an examplegraphical representation of category-scores for the post.

FIG. 7 illustrates an example method for categorizing objects based on aplurality of category-scores.

FIG. 8 illustrates an example method for categorizing objects based on aplurality of category-scores using a pre-filtering process.

FIG. 9 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with asocial-networking system. Network environment 100 includes a clientsystem 130, a social-networking system 160, and a third-party system 170connected to each other by a network 110. Although FIG. 1 illustrates aparticular arrangement of a client system 130, a social-networkingsystem 160, a third-party system 170, and a network 110, this disclosurecontemplates any suitable arrangement of a client system 130, asocial-networking system 160, a third-party system 170, and a network110. As an example and not by way of limitation, two or more of a clientsystem 130, a social-networking system 160, and a third-party system 170may be connected to each other directly, bypassing a network 110. Asanother example, two or more of a client system 130, a social-networkingsystem 160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130,social-networking systems 160, third-party systems 170, and networks110, this disclosure contemplates any suitable number of client systems130, social-networking systems 160, third-party systems 170, andnetworks 110. As an example and not by way of limitation, networkenvironment 100 may include multiple client systems 130,social-networking systems 160, third-party systems 170, and networks110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, a social-networking system160, and a third-party system 170 to a communication network 110 or toeach other. This disclosure contemplates any suitable links 150. Inparticular embodiments, one or more links 150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, other suitable electronicdevice, or any suitable combination thereof. This disclosurecontemplates any suitable client systems 130. A client system 130 mayenable a network user at a client system 130 to access a network 110. Aclient system 130 may enable its user to communicate with other users atother client systems 130.

In particular embodiments, a client system 130 may include a web browser132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLAFIREFOX, and may have one or more add-ons, plug-ins, or otherextensions, such as TOOLBAR or YAHOO TOOLBAR. A user at a client system130 may enter a Uniform Resource Locator (URL) or other addressdirecting a web browser 132 to a particular server (such as server 162,or a server associated with a third-party system 170), and the webbrowser 132 may generate a Hyper Text Transfer Protocol (HTTP) requestand communicate the HTTP request to server. The server may accept theHTTP request and communicate to a client system 130 one or more HyperText Markup Language (HTML) files responsive to the HTTP request. Theclient system 130 may render a web interface (e.g. a webpage) based onthe HTML files from the server for presentation to the user. Thisdisclosure contemplates any suitable source files. As an example and notby way of limitation, a web interface may be rendered from HTML files,Extensible Hyper Text Markup Language (XHTML) files, or ExtensibleMarkup Language (XML) files, according to particular needs. Suchinterfaces may also execute scripts such as, for example and withoutlimitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT,combinations of markup language and scripts such as AJAX (AsynchronousJAVASCRIPT and XML), and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Social Graphs

FIG. 2 illustrates an example social graph 200. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 200 in one or more data stores. In particular embodiments,the social graph 200 may include multiple nodes—which may includemultiple user nodes 202 or multiple concept nodes 204—and multiple edges206 connecting the nodes. The example social graph 200 illustrated inFIG. 2 is shown, for didactic purposes, in a two-dimensional visual maprepresentation. In particular embodiments, a social-networking system160, a client system 130, or a third-party system 170 may access thesocial graph 200 and related social-graph information for suitableapplications. The nodes and edges of the social graph 200 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 200.

In particular embodiments, a user node 202 may correspond to a user ofthe social-networking system 160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or overthe social-networking system 160. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 202 corresponding tothe user, and store the user node 202 in one or more data stores. Usersand user nodes 202 described herein may, where appropriate, refer toregistered users and user nodes 202 associated with registered users. Inaddition or as an alternative, users and user nodes 202 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 202may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 202 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 202 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node204 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160. As an example and not by way oflimitation, information of a concept may include a name or a title; oneor more images (e.g., an image of the cover page of a book); a location(e.g., an address or a geographical location); a website (which may beassociated with a URL); contact information (e.g., a phone number or anemail address); other suitable concept information; or any suitablecombination of such information. In particular embodiments, a conceptnode 204 may be associated with one or more data objects correspondingto information associated with concept node 204. In particularembodiments, a concept node 204 may correspond to one or more webinterfaces.

In particular embodiments, a node in the social graph 200 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160. Profile interfaces may also behosted on third-party websites associated with a third-party server 170.As an example and not by way of limitation, a profile interfacecorresponding to a particular external web interface may be theparticular external web interface and the profile interface maycorrespond to a particular concept node 204. Profile interfaces may beviewable by all or a selected subset of other users. As an example andnot by way of limitation, a user node 202 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 204 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 204.

In particular embodiments, a concept node 204 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 202 corresponding to the user and a conceptnode 204 corresponding to the third-party web interface or resource andstore edge 206 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 200 maybe connected to each other by one or more edges 206. An edge 206connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 206 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 206 connecting the first user's user node 202 to thesecond user's user node 202 in the social graph 200 and store edge 206as social-graph information in one or more of data stores 164. In theexample of FIG. 2, the social graph 200 includes an edge 206 indicatinga friend relation between user nodes 202 of user “A” and user “B” and anedge indicating a friend relation between user nodes 202 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 206 with particular attributes connecting particular user nodes202, this disclosure contemplates any suitable edges 206 with anysuitable attributes connecting user nodes 202. As an example and not byway of limitation, an edge 206 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), subscriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and aconcept node 204 may represent a particular action or activity performedby a user associated with user node 202 toward a concept associated witha concept node 204. As an example and not by way of limitation, asillustrated in FIG. 2, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 204 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (SPOTIFY, which is an onlinemusic application). In this case, the social-networking system 160 maycreate a “listened” edge 206 and a “used” edge (as illustrated in FIG.2) between user nodes 202 corresponding to the user and concept nodes204 corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 206 (asillustrated in FIG. 2) between concept nodes 204 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 206corresponds to an action performed by an external application (SPOTIFY)on an external audio file (the song “Imagine”). Although this disclosuredescribes particular edges 206 with particular attributes connectinguser nodes 202 and concept nodes 204, this disclosure contemplates anysuitable edges 206 with any suitable attributes connecting user nodes202 and concept nodes 204. Moreover, although this disclosure describesedges between a user node 202 and a concept node 204 representing asingle relationship, this disclosure contemplates edges between a usernode 202 and a concept node 204 representing one or more relationships.As an example and not by way of limitation, an edge 206 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 206 may represent each type of relationship(or multiples of a single relationship) between a user node 202 and aconcept node 204 (as illustrated in FIG. 2 between user node 202 foruser “E” and concept node 204 for “SPOTIFY”).

In particular embodiments, the social-networking system 160 may createan edge 206 between a user node 202 and a concept node 204 in the socialgraph 200. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 204 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 206 between user node202 associated with the user and concept node 204, as illustrated by“like” edge 206 between the user and concept node 204. In particularembodiments, the social-networking system 160 may store an edge 206 inone or more data stores. In particular embodiments, an edge 206 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 206 may be formed between user node 202 corresponding to thefirst user and concept nodes 204 corresponding to those concepts.Although this disclosure describes forming particular edges 206 inparticular manners, this disclosure contemplates forming any suitableedges 206 in any suitable manner.

Search Queries on Online Social Networks

In particular embodiments, the social-networking system 160 may receive,from a client system of a user of an online social network, a queryinputted by the user. The user may submit the query to thesocial-networking system 160 by, for example, selecting a query input orinputting text into query field. A user of an online social network maysearch for information relating to a specific subject matter (e.g.,users, concepts, external content or resource) by providing a shortphrase describing the subject matter, often referred to as a “searchquery,” to a search engine. The query may be an unstructured text queryand may comprise one or more text strings (which may include one or moren-grams). In general, a user may input any character string into a queryfield to search for content on the social-networking system 160 thatmatches the text query. The social-networking system 160 may then searcha data store 164 (or, in particular, a social-graph database) toidentify content matching the query. The search engine may conduct asearch based on the query phrase using various search algorithms andgenerate search results that identify resources or content (e.g.,user-profile interfaces, content-profile interfaces, or externalresources) that are most likely to be related to the search query. Toconduct a search, a user may input or send a search query to the searchengine. In response, the search engine may identify one or moreresources that are likely to be related to the search query, each ofwhich may individually be referred to as a “search result,” orcollectively be referred to as the “search results” corresponding to thesearch query. The identified content may include, for example,social-graph elements (i.e., user nodes 202, concept nodes 204, edges206), profile interfaces, external web interfaces, or any combinationthereof. The social-networking system 160 may then generate asearch-results interface with search results corresponding to theidentified content and send the search-results interface to the user.The search results may be presented to the user, often in the form of alist of links on the search-results interface, each link beingassociated with a different interface that contains some of theidentified resources or content. In particular embodiments, each link inthe search results may be in the form of a Uniform Resource Locator(URL) that specifies where the corresponding interface is located andthe mechanism for retrieving it. The social-networking system 160 maythen send the search-results interface to the web browser 132 on theuser's client system 130. The user may then click on the URL links orotherwise select the content from the search-results interface to accessthe content from the social-networking system 160 or from an externalsystem (such as, for example, a third-party system 170), as appropriate.The resources may be ranked and presented to the user according to theirrelative degrees of relevance to the search query. The search resultsmay also be ranked and presented to the user according to their relativedegree of relevance to the user. In other words, the search results maybe personalized for the querying user based on, for example,social-graph information, user information, search or browsing historyof the user, or other suitable information related to the user. Inparticular embodiments, ranking of the resources may be determined by aranking algorithm implemented by the search engine. As an example andnot by way of limitation, resources that are more relevant to the searchquery or to the user may be ranked higher than the resources that areless relevant to the search query or the user. In particularembodiments, the search engine may limit its search to resources andcontent on the online social network. However, in particularembodiments, the search engine may also search for resources or contentson other sources, such as a third-party system 170, the internet orWorld Wide Web, or other suitable sources. Although this disclosuredescribes querying the social-networking system 160 in a particularmanner, this disclosure contemplates querying the social-networkingsystem 160 in any suitable manner.

Typeahead Processes and Queries

In particular embodiments, one or more client-side and/or backend(server-side) processes may implement and utilize a “typeahead” featurethat may automatically attempt to match social-graph elements (e.g.,user nodes 202, concept nodes 204, or edges 206) to informationcurrently being entered by a user in an input form rendered inconjunction with a requested interface (such as, for example, auser-profile interface, a concept-profile interface, a search-resultsinterface, a user interface/view state of a native applicationassociated with the online social network, or another suitable interfaceof the online social network), which may be hosted by or accessible inthe social-networking system 160. In particular embodiments, as a useris entering text to make a declaration, the typeahead feature mayattempt to match the string of textual characters being entered in thedeclaration to strings of characters (e.g., names, descriptions)corresponding to users, concepts, or edges and their correspondingelements in the social graph 200. In particular embodiments, when amatch is found, the typeahead feature may automatically populate theform with a reference to the social-graph element (such as, for example,the node name/type, node ID, edge name/type, edge ID, or anothersuitable reference or identifier) of the existing social-graph element.In particular embodiments, as the user enters characters into a formbox, the typeahead process may read the string of entered textualcharacters. As each keystroke is made, the frontend-typeahead processmay send the entered character string as a request (or call) to thebackend-typeahead process executing within the social-networking system160. In particular embodiments, the typeahead process may use one ormore matching algorithms to attempt to identify matching social-graphelements. In particular embodiments, when a match or matches are found,the typeahead process may send a response to the user's client system130 that may include, for example, the names (name strings) ordescriptions of the matching social-graph elements as well as,potentially, other metadata associated with the matching social-graphelements. As an example and not by way of limitation, if a user entersthe characters “pok” into a query field, the typeahead process maydisplay a drop-down menu that displays names of matching existingprofile interfaces and respective user nodes 202 or concept nodes 204,such as a profile interface named or devoted to “poker” or “pokemon,”which the user can then click on or otherwise select thereby confirmingthe desire to declare the matched user or concept name corresponding tothe selected node.

More information on typeahead processes may be found in U.S. patentapplication Ser. No. 12/763162, filed 19 Apr. 2010, and U.S. patentapplication Ser. No. 13/556072, filed 23 Jul. 2012, which areincorporated by reference.

In particular embodiments, the typeahead processes described herein maybe applied to search queries entered by a user. As an example and not byway of limitation, as a user enters text characters into a query field,a typeahead process may attempt to identify one or more user nodes 202,concept nodes 204, or edges 206 that match the string of charactersentered into the query field as the user is entering the characters. Asthe typeahead process receives requests or calls including a string orn-gram from the text query, the typeahead process may perform or causeto be performed a search to identify existing social-graph elements(i.e., user nodes 202, concept nodes 204, edges 206) having respectivenames, types, categories, or other identifiers matching the enteredtext. The typeahead process may use one or more matching algorithms toattempt to identify matching nodes or edges. When a match or matches arefound, the typeahead process may send a response to the user's clientsystem 130 that may include, for example, the names (name strings) ofthe matching nodes as well as, potentially, other metadata associatedwith the matching nodes. The typeahead process may then display adrop-down menu that displays names of matching existing profileinterfaces and respective user nodes 202 or concept nodes 204, anddisplays names of matching edges 206 that may connect to the matchinguser nodes 202 or concept nodes 204, which the user can then click on orotherwise select thereby confirming the desire to search for the matcheduser or concept name corresponding to the selected node, or to searchfor users or concepts connected to the matched users or concepts by thematching edges. Alternatively, the typeahead process may simplyauto-populate the form with the name or other identifier of thetop-ranked match rather than display a drop-down menu. The user may thenconfirm the auto-populated declaration simply by keying “enter” on akeyboard or by clicking on the auto-populated declaration. Upon userconfirmation of the matching nodes and edges, the typeahead process maysend a request that informs the social-networking system 160 of theuser's confirmation of a query containing the matching social-graphelements. In response to the request sent, the social-networking system160 may automatically (or alternately based on an instruction in therequest) call or otherwise search a social-graph database for thematching social-graph elements, or for social-graph elements connectedto the matching social-graph elements as appropriate. Although thisdisclosure describes applying the typeahead processes to search queriesin a particular manner, this disclosure contemplates applying thetypeahead processes to search queries in any suitable manner.

In connection with search queries and search results, particularembodiments may utilize one or more systems, components, elements,functions, methods, operations, or steps disclosed in U.S. patentapplication Ser. No. 11/503093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978265, filed 23 Dec. 2010, U.S. patentapplication Ser. No. 14/304596, filed 13 Jun. 2014, U.S. patentapplication Ser. No.14/452307, filed 5 Aug. 2014, U.S. patentapplication Ser. No. 14/745001, filed 19 Jun. 2015, U.S. patentapplication Ser. No. 14/826868, filed 14 Aug. 2015, and U.S. patentapplication Ser. No. 14/454826, filed 8 Aug. 2016 which are incorporatedby reference.

Structured Search Queries

In particular embodiments, in response to a text query received from afirst user (i.e., the querying user), the social-networking system 160may parse the text query and identify portions of the text query thatcorrespond to particular social-graph elements. However, in some cases aquery may include one or more terms that are ambiguous, where anambiguous term is a term that may possibly correspond to multiplesocial-graph elements. To parse the ambiguous term, thesocial-networking system 160 may access a social graph 200 and thenparse the text query to identify the social-graph elements thatcorresponded to ambiguous n-grams from the text query. Thesocial-networking system 160 may then generate a set of structuredqueries, where each structured query corresponds to one of the possiblematching social-graph elements. These structured queries may be based onstrings generated by a grammar model, such that they are rendered in anatural-language syntax with references to the relevant social-graphelements. As an example and not by way of limitation, in response to thetext query, “show me friends of my girlfriend,” the social-networkingsystem 160 may generate a structured query “Friends of Stephanie,” where“Friends” and “Stephanie” in the structured query are referencescorresponding to particular social-graph elements. The reference to“Stephanie” would correspond to a particular user node 202 (where thesocial-networking system 160 has parsed the n-gram “my girlfriend” tocorrespond with a user node 202 for the user “Stephanie”), while thereference to “Friends” would correspond to friend-type edges 206connecting that user node 202 to other user nodes 202 (i.e., edges 206connecting to “Stephanie's” first-degree friends). When executing thisstructured query, the social-networking system 160 may identify one ormore user nodes 202 connected by friend-type edges 206 to the user node202 corresponding to “Stephanie”. As another example and not by way oflimitation, in response to the text query, “friends who work atfacebook,” the social-networking system 160 may generate a structuredquery “My friends who work at Facebook,” where “my friends,” “work at,”and “Facebook” in the structured query are references corresponding toparticular social-graph elements as described previously (i.e., afriend-type edge 206, a work-at-type edge 206, and concept node 204corresponding to the company “Facebook”). By providing suggestedstructured queries in response to a user's text query, thesocial-networking system 160 may provide a powerful way for users of theonline social network to search for elements represented in the socialgraph 200 based on their social-graph attributes and their relation tovarious social-graph elements. Structured queries may allow a queryinguser to search for content that is connected to particular users orconcepts in the social graph 200 by particular edge-types. Thestructured queries may be sent to the first user and displayed in adrop-down menu (via, for example, a client-side typeahead process),where the first user can then select an appropriate query to search forthe desired content. Some of the advantages of using the structuredqueries described herein include finding users of the online socialnetwork based upon limited information, bringing together virtualindexes of content from the online social network based on the relationof that content to various social-graph elements, or finding contentrelated to you and/or your friends. Although this disclosure describesgenerating particular structured queries in a particular manner, thisdisclosure contemplates generating any suitable structured queries inany suitable manner.

More information on element detection and parsing queries may be foundin U.S. patent application Ser. No. 13/556072, filed 23 Jul. 2012, U.S.patent application Ser. No. 13/731866, filed 31 Dec. 2012, and U.S.patent application Ser. No. 13/732101, filed 31 Dec. 2012, each of whichis incorporated by reference. More information on structured searchqueries and grammar models may be found in U.S. patent application Ser.No. 13/556072, filed 23 Jul. 2012, U.S. patent application Ser. No.13/674695, filed 12 Nov. 2012, and U.S. patent application Ser. No.13/731866, filed 31 Dec. 2012, each of which is incorporated byreference.

Generating Keywords and Keyword Queries

In particular embodiments, the social-networking system 160 may providecustomized keyword completion suggestions to a querying user as the useris inputting a text string into a query field. Keyword completionsuggestions may be provided to the user in a non-structured format. Inorder to generate a keyword completion suggestion, the social-networkingsystem 160 may access multiple sources within the social-networkingsystem 160 to generate keyword completion suggestions, score the keywordcompletion suggestions from the multiple sources, and then return thekeyword completion suggestions to the user. As an example and not by wayof limitation, if a user types the query “friends stan,” then thesocial-networking system 160 may suggest, for example, “friendsstanford,” “friends stanford university,” “friends stanley,” “friendsstanley cooper,” “friends stanley kubrick,” “friends stanley cup,” and“friends stanlonski.” In this example, the social-networking system 160is suggesting the keywords which are modifications of the ambiguousn-gram “stan,” where the suggestions may be generated from a variety ofkeyword generators. The social-networking system 160 may have selectedthe keyword completion suggestions because the user is connected in someway to the suggestions. As an example and not by way of limitation, thequerying user may be connected within the social graph 200 to theconcept node 204 corresponding to Stanford University, for example bylike- or attended-type edges 206. The querying user may also have afriend named Stanley Cooper. Although this disclosure describesgenerating keyword completion suggestions in a particular manner, thisdisclosure contemplates generating keyword completion suggestions in anysuitable manner.

More information on keyword queries may be found in U.S. patentapplication Ser. No. 14/244748, filed 3 Apr. 2014, U.S. patentapplication Ser. No. 14/470607, filed 27 Aug. 2014, and U.S. patentapplication Ser. No. 14/561418, filed 5 Dec. 2014, each of which isincorporated by reference.

Indexing Based on Object-type

FIG. 3 illustrates an example partitioning for storing objects ofsocial-networking system 160. A plurality of data stores 164 (which mayalso be called “verticals”) may store objects of social-networkingsystem 160. The amount of data (e.g., data for a social graph 200)stored in the data stores may be very large. As an example and not byway of limitation, a social graph used by Facebook, Inc. of Menlo Park,Calif. can have a number of nodes in the order of 10 ⁸, and a number ofedges in the order of 10 ¹⁰. Typically, a large collection of data suchas a large database may be divided into a number of partitions. As theindex for each partition of a database is smaller than the index for theoverall database, the partitioning may improve performance in accessingthe database. As the partitions may be distributed over a large numberof servers, the partitioning may also improve performance andreliability in accessing the database. Ordinarily, a database may bepartitioned by storing rows (or columns) of the database separately. Inparticular embodiments, a database maybe partitioned based onobject-types. Data objects may be stored in a plurality of partitions,each partition holding data objects of a single object-type. Inparticular embodiments, social-networking system 160 may retrieve searchresults in response to a search query by submitting the search query toa particular partition storing objects of the same object-type as thesearch query's expected results. Although this disclosure describesstoring objects in a particular manner, this disclosure contemplatesstoring objects in any suitable manner.

In particular embodiments, each object may correspond to a particularnode of a social graph 200. An edge 206 connecting the particular nodeand another node may indicate a relationship between objectscorresponding to these nodes. In addition to storing objects, aparticular data store may also store social-graph information relatingto the object. Alternatively, social-graph information about particularobjects may be stored in a different data store from the objects.Social-networking system 160 may update the search index of the datastore based on newly received objects, and relationships associated withthe received objects.

In particular embodiments, each data store 164 may be configured tostore objects of a particular one of a plurality of object-types inrespective data storage devices 340. An object-type may be, for example,a user, a photo, a post, a comment, a message, an event listing, a webinterface, an application, a location, a user-profile interface, aconcept-profile interface, a user group, an audio file, a video, anoffer/coupon, or another suitable type of object. Although thisdisclosure describes particular types of objects, this disclosurecontemplates any suitable types of objects. As an example and not by wayof limitation, a user vertical P1 illustrated in FIG. 3 may store userobjects. Each user object stored in the user vertical P1 may comprise anidentifier (e.g., a character string), a user name, and a profilepicture for a user of the online social network. Social-networkingsystem 160 may also store in the user vertical P1 information associatedwith a user object such as language, location, education, contactinformation, interests, relationship status, a list of friends/contacts,a list of family members, privacy settings, and so on. As an example andnot by way of limitation, a post vertical P2 illustrated in FIG. 3 maystore post objects. Each post object stored in the post vertical P2 maycomprise an identifier, a text string for a post posted tosocial-networking system 160. Social-networking system 160 may alsostore in the post vertical P2 information associated with a post objectsuch as a time stamp, an author, privacy settings, users who like thepost, a count of likes, comments, a count of comments, location, and soon. As an example and not by way of limitation, a photo vertical P3 maystore photo objects (or objects of other media types such as video oraudio). Each photo object stored in the photo vertical P3 may comprisean identifier and a photo. Social-networking system 160 may also storein the photo vertical P3 information associated with a photo object suchas a time stamp, an author, privacy settings, users who are tagged inthe photo, users who like the photo, comments, and so on. In particularembodiments, each data store may also be configured to store informationassociated with each stored object in data storage devices 340.

In particular embodiments, objects stored in each vertical 164 may beindexed by one or more search indices. The search indices may be hostedby respective index server 330 comprising one or more computing devices(e.g., servers). The index server 330 may update the search indicesbased on data (e.g., a photo and information associated with a photo)submitted to social-networking system 160 by users or other processes ofsocial-networking system 160 (or a third-party system). The index server330 may also update the search indices periodically (e.g., every 24hours). The index server 330 may receive a query comprising a searchterm, and access and retrieve search results from one or more searchindices corresponding to the search term. In some embodiments, avertical corresponding to a particular object-type may comprise aplurality of physical or logical partitions, each comprising respectivesearch indices.

In particular embodiments, social-networking system 160 may receive asearch query from a PHP (Hypertext Preprocessor) process 310. The PHPprocess 310 may comprise one or more computing processes hosted by oneor more servers 162 of social-networking system 160. The search querymay be a text string or a search query submitted to the PHP process by auser or another process of social-networking system 160 (or third-partysystem 170). In particular embodiments, an aggregator 320 may beconfigured to receive the search query from PHP process 310 anddistribute the search query to each vertical. The aggregator maycomprise one or more computing processes (or programs) hosted by one ormore computing devices (e.g. servers) of the social-networking system160. Particular embodiments may maintain the plurality of verticals 164as illustrated in FIG. 3. Each of the verticals 164 may be configured tostore a single type of object indexed by a search index as describedearlier. In particular embodiments, the aggregator 320 may receive asearch request. For example, the aggregator 320 may receive a searchrequest from a PHP (Hypertext Preprocessor) process 210 illustrated inFIG. 2. In particular embodiments, the search request may comprise atext string. The search request may be a structured or substantiallyunstructured text string submitted by a user via a PHP process. Thesearch request may also be structured or a substantially unstructuredtext string received from another process of the social-networkingsystem. In particular embodiments, the aggregator 320 may determine oneor more search queries based on the received search request (step 303).In particular embodiments, each of the search queries may have a singleobject type for its expected results (i.e., a single result-type). Inparticular embodiments, the aggregator 320 may, for each of the searchqueries, access and retrieve search query results from at least one ofthe verticals 164, wherein the at least one vertical 164 is configuredto store objects of the object type of the search query (i.e., theresult-type of the search query). In particular embodiments, theaggregator 320 may aggregate search query results of the respectivesearch queries. For example, the aggregator 320 may submit a searchquery to a particular vertical and access index server 330 of thevertical, causing index server 330 to return results for the searchquery.

More information on indexes and search queries may be found in U.S.patent application Ser. No. 13/560212, filed 27 Jul. 2012, U.S. patentapplication Ser. No. 13/560901, filed 27 Jul. 2012, U.S. patentapplication Ser. No. 13/723861, filed 21 Dec. 2012, and U.S. patentapplication Ser. No. 13/870113, filed 25 Apr. 2013, each of which isincorporated by reference.

Categorizing Objects for Queries

In particular embodiments, objects corresponding to search results maybe grouped into various categories and ranked within each category basedon multiple axes or factors. The ranking of an object with respect to acategory may be calculated as a weighting of scores based with respectto the multiple axes, where the weighting may be based on the category.The categories may be defined in a variety of ways and each object maybe scored or ranked differently for each category (e.g., scoring poststo determine if they are highly “newsy” or “celebrity”, etc.). As anexample and not by way of limitation, there may be a domain expertcategory, a celebrity category, a review category, a how-to category, acommentary category, a news category, or any other suitable category.Previously, search results would be returned in an aggregate listwithout regard to a category. This previous method may have resulted inmany dissimilar results being returned into one disorganized list withaverage results (e.g., results that have middling scores with respect tomultiple axes, but possibly not including results with a high score withrespect to a single axis). The embodiments described herein may have thebenefit of identifying the best search results for each category (e.g.,the best “news” results, the best “celebrity” result, etc.), and the topresults from each category may be presented to the user as a listorganized by category.

In particular embodiments, the social-networking system 160 may receive,from a client system 130 of a user of an online social network, a queryinputted by the user. In particular embodiments, the social-networkingsystem 160 may identify a set of objects associated with the onlinesocial network matching the query. The social-networking system 160 maysearch a data store 164 (i.e., vertical) to identify content matchingthe query. In particular embodiments, identifying the set of objectsassociated with the online social network matching the query maycomprise searching a plurality of verticals 164 to identify a pluralityof sub-sets of objects from the plurality of verticals 164,respectively, that match the search query. Each vertical 164 may storeobjects of a particular object-type. The object-types stored by avertical 164 may include, for example, a user, a photo, a post, acomment, a message, an event listing, a web interface, an application, alocation, a user-profile interface, a concept-profile interface, a usergroup, an audio file, a video, an offer/coupon, or another suitable typeof object. Although this disclosure describes particular types ofobjects and verticals, this disclosure contemplates any suitable objectsand any suitable verticals.

FIG. 4A illustrates an example set of K weighted sub-scorescorresponding to an example set of C categories for an exampleidentified object. In particular embodiments, the social-networkingsystem 160 may calculate, for each identified object, a plurality ofcategory-scores corresponding to a plurality of categories,respectively. In this example, there are C categories, and each ofcategory_1 to cateogry_C may correspond to a particular category of theC categories. To calculate a category-score, the social-networkingsystem 160 may calculate a plurality of sub-scores, which may bereferred to as “axes” or “scoring axes,” and then the weightedsub-scores may be used to calculated the category-score. In thisexample, there are K scoring axes, and each sub-score may correspond toa scoring axis of axis_1 to axis_K. R(k) may be the sub-score of theidentified object with respect to the k^(th) axis, and a(k, c) may be aweight of the k^(th) sub-score with respect to the c^(th) category.

In particular embodiments, each category-score may be calculated basedon a plurality of sub-scores corresponding to a plurality of scoringaxes, respectively. Each scoring axis may correspond to a particularfactor associated with the respective scoring axis. The factorsassociated with a scoring axis may include, for example, social-graphinformation (such as, for example, degree of separation betweensocial-graph nodes, social-graph affinity, or social relevance, each ofwhich may be its own axis), recency, topic relevance, author quality,text similarity, popularity, proximity, a user's search history, orother suitable factors, or any suitable combination thereof. Eachcategory-score may be calculated based on a weighting of the pluralityof sub-scores. As an example and not be way of limitation,category-scores may be calculated as a weighted average, a weightedproduct, or any other suitable weighting or combination thereof. Theweighting for each sub-score may be based on the category correspondingto the respective category-score. A set of weights may be used whenscoring with respect to a particular category, where the weights mayemphasize factors that are important for the category and deemphasizefactors unimportant for the category. As an example and not by way oflimitation, a news category may weight a recency factor and an authorquality factor higher than other factors based on the relative importantof those factors to the news category. As an example and not by way oflimitation, referencing FIG. 4A, each a(k, c) may be a weighting foreach sub-score R (k) based on the category. For a particular category C,a higher value of a(k, C) may correspond to a particular factorassociated with axis k having more weight or importance when scoringwith respect to category C. In particular embodiments, the weighting ofthe plurality of sub-scores for at least one category-score may comprisea weighted arithmetic mean of the plurality of sub-scores. As an exampleand not by way of limitation, the category-score for a particularcategory C for the example identified object in FIG. 4A may becalculated as

$\frac{\sum_{k}{{a\left( {k,C} \right)}{R(k)}}}{\sum_{k}{a\left( {k,C} \right)}}.$

In particular embodiments, for an identified object, the sub-scorecorresponding to at least one of the scoring axes may be based on asocial-graph affinity of the user with respect to the identified object.As an example and not by way of limitation, in response to a query“Photos of my friends,” the social-networking system 160 may identifyphoto-type content objects in a photos vertical 164, where eachidentified photo is tagged with a least one user who is a friend of thequerying user (i.e., users corresponding to user nodes 202 connected byfriend-type edges 206 to the user node 202 corresponding to the queryinguser). When calculating sub-scores for identified concept nodes 204corresponding to photos with the user's friends tagged in the photo, thesocial-networking system 160 may score photos based on the social-graphaffinity (e.g., as measured by an affinity coefficient) of the userstagged in the photo with respect to the querying user. Furthermore,photos showing more of the querying user's friends may have a higheraffinity score than photos showing fewer of the user's friends, sincehaving more friends tagged in the photo may increase the querying user'saffinity with respect to that particular photo. As another example andnot by way of limitation, in response to a query from a user <Mark>, thesocial-networking system 160 may identify a set of objects that includesusers <Tom>, <Dick>, and <Harry>. The social-networking system 160 maythen score the users <Tom>, <Dick>, and <Harry> based on theirrespective social-graph affinity with respect to the querying user<Mark>. For example, the social-networking system 160 may score theidentified nodes of users <Tom>, <Dick>, and <Harry> based in part on anumber of posts authored by those users and liked by the user <Mark>. Ifuser <Dick> authored three posts that were liked by the user <Mark>,user <Tom> authored two posts liked by <Mark>, and user <Harry> authoredone post like by <Mark>, the social-networking system 160 may score user<Dick> as highest with respect to an affinity-score axis since heauthored most of the posts liked by the user <Mark>, with <Tom> and<Harry> having consecutively lower scores. Although this disclosuredescribes calculating sub-scores for objects based on social-graphaffinity in a particular manner, this disclosure contemplatescalculating sub-scores for objects based on social-graph affinity in anysuitable manner.

In particular embodiments, for an identified object, the sub-scorecorresponding to at least one of the scoring axes may be based on acalculated text similarity between the identified object and the query.The text similarity or textual relevance of a query may be based on howthe terms and number of terms in the query match to text associated withan identified object. In particular embodiments, a text-similaritysub-score may be based on matches between a query and words or phrasesassociated with an identified object (e.g., summary, subject, title,author, keywords, or body of text associated with an identified object).In particular embodiments, a text-similarity sub-score may be based on anumber of text matches between a query and text associated with anidentified object. As an example and not by way of limitation, anidentified object that includes 80% of the terms of a query may have ahigher text-similarity sub-score than another identified object thatincludes 50% of the terms. As another example and not by way oflimitation, if a user submits a query “Hawaii bike rides,” a post thatincludes the phrase “bike rides in Hawaii” may have a relatively hightext-similarity sub-score (e.g., 1.0 out of 1.0), while a post thatincludes the phrase “bike-riding vacations” may have a lowertext-similarity score (e.g., 0.6 out of 1.0). In particular embodiments,a text-similarity sub-score may be based on a number of times text froma query occurs in text associated with an identified object. Forexample, if a user submits a query “coffee shops in San Francisco,” anidentified object that includes the terms “coffee” or “coffee shop” 5times may have a higher text-similarity sub-score than anotheridentified object that includes “coffee” 1 time. In particularembodiments, a text-similarity sub-score may be based on a termfrequency-inverse document frequency (TF-IDF) metric. A termfrequency-inverse document frequency metric may increase based on thenumber of times a query term appears in an identified object, butdecrease based on the frequency of the query term across a plurality ofobjects. Although this disclosure describes calculating sub-scores forobjects based on text similarity in a particular manner, this disclosurecontemplates calculating sub-scores for objects based on text similarityin any suitable manner.

In particular embodiments, for an identified object, the sub-scorecorresponding to at least one of the scoring axes may be based on arecency value associated with the identified object. A recency value maycorrespond to how recently an associated object was generated, created,posted, sent, received, viewed, or commented on. For example, a recencyvalue associated with an identified object may be determined based on atime or date associated with the object compared with the current timeor date. Objects associated with more recent dates may have higherrecency-value sub-scores than objects associated with dates further inthe past. A recency value may be calculated using a decay function, suchas a Gaussian decay function, a linear decay function, an exponentialdecay function, or any other suitable function. As an example and not byway of limitation, an identified object that was posted two days ago mayhave a relatively high recency-value sub-score (e.g., 0.9 out of 1.0),while another identified object that was posted a year ago may have arelatively low recency-value sub-score (e.g., 0.2 out of 1.0). Inparticular embodiments, a recency value may correspond to a time or dateassociated with a future event or activity, such that an event occurringsooner in the future may have a higher recency-value sub-score than anevent happening further in the future. As an example and not by way oflimitation, an identified object corresponding to a party happeningtomorrow may have a higher recency-value sub-score than anotheridentified object corresponding to a concert happening two weeks in thefuture. Although this disclosure describes calculating sub-scores forobjects based on recency in a particular manner, this disclosurecontemplates calculating sub-scores for objects based on recency in anysuitable manner.

In particular embodiments, for an identified object, the sub-scorecorresponding to at least one of the scoring axes may be based on acalculated topic relevance for the identified object with respect to thequery. The social-networking system 160 may determine one or more topicsrelated to a search query and one or more topics associated with theidentified object. As an example and not by way of limitation, thesocial-networking system 160 may utilize a topic tagger to identifytopics associated with identified objects, as disclosed in U.S. patentapplication Ser. No. 14/470583, filed 27 Aug. 2014, which isincorporated herein by reference. Objects with topics associated withthe search query may receive a higher topic-relevance sub-score thanobjects with unrelated topics. As an example and not by way oflimitation, for a search query related to the television show GAME OFTHRONES (e.g., “Tyrion Lannister actor”), objects associated with arelated topic (e.g., an article about David Benioff, the show runner ofGAME OF THRONES) may have a higher topic-relevance sub-score than anobject associated with an unrelated topic. Although this disclosuredescribes calculating sub-scores for objects based on topic relevance ina particular manner, this disclosure contemplates calculating sub-scoresfor objects based on topic relevance in any suitable manner.

In particular embodiments, for an identified object, the sub-scorecorresponding to at least one of the scoring axes may be based on acalculated author quality associated with the identified object. As anexample and not by way of limitation, an identified object may have ahigher author-quality sub-score if it is associated with a popularauthor, while another identified object associated with a less popularauthor may have a lower author-quality sub-score. In particularembodiments, an author-quality sub-score may be calculated with respectto a topic. As an example and not by way of limitation, for a searchquery related to STAR WARS (e.g., “Wookiee planet”), an identifiedobject may have a higher author-quality sub-score if it is authored byGeorge Lucas, the creator of STAR WARS, while another identified objectassociated with an author unrelated to the query may have a lowerauthor-quality sub-score. In particular embodiments, an author-qualitysub-score may be based in part on a number of “likes” or views an authorhas received or a measure of the author's global popularity on theonline social network. In particular embodiments, an author-qualitysub-score may be based in part on a number of connecting edges 206 tonodes associated with a particular author. For example, an authorassociated with nodes having more connecting edges 206 may be morepopular and may have a higher author-quality sub-score than anotherauthor associated with nodes having fewer connecting edges 206. Inparticular embodiments, an author-quality sub-score may be based in parton an author's popularity with respect to the querying user or friendsof the querying user. For example, an identified object associated withan author who has received a greater number of “likes” from friends of aquerying user may receive a higher author-quality sub-score than anotherauthor who has received fewer “likes” from friends of the querying user.Although this disclosure describes calculating sub-scores for objectsbased on author quality in a particular manner, this disclosurecontemplates calculating sub-scores for objects based on author qualityin any suitable manner.

In particular embodiments, for an identified object, at least onesub-score may be based on a degree of separation in a social graph 200between the user node 202 corresponding to the querying user and a nodecorresponding to the identified object. As an example and not by way oflimitation, a sub-score for an identified object may be higher if theuser node 202 corresponding to the querying user and the nodecorresponding to an identified object have a smaller degree ofseparation. For example, an identified object associated with theWASHINGTON POST may have a higher author-quality sub-score for a userwho has liked the WASHINGTON POST in the past compared to a user who hasnot. Although this disclosure describes calculating sub-scores forobjects based on a degree of separation in a particular manner, thisdisclosure contemplates calculating sub-scores for objects based on adegree of separation in any suitable manner.

FIG. 4B illustrates an example set of particular weighted sub-scorescorresponding to an example set of particular categories for an exampleidentified object. In particular embodiments, each scoring axis maycorrespond to a particular factor associated with the respective scoringaxis. In the example illustrated in FIG. 4B, four example scoring axesare illustrated, corresponding to the particular factors of recency,social relevance, text similarity, and author quality. Although FIG. 4Billustrates particular scoring axes, this disclosure contemplates anysuitable scoring axes corresponding to any suitable factor. As anexample and not by way of limitation, a scoring axis may correspond tofactors associated with social-graph information (such as, for example,degree of separation between social-graph nodes, social-graph affinity,or social relevance, each of which may be its own axis), recency, topicrelevance, author quality, text similarity, popularity, proximity, auser's search history, or other suitable criteria, or any suitablecombination thereof.

In particular embodiments, each category-score may be calculated basedon a plurality of sub-scores corresponding to a plurality of scoringaxes. In particular embodiments, each category-score may be calculatedbased on a weighting of the plurality of sub-scores. In this example,four categories are illustrated, corresponding to a news category, acelebrity category, a how-to category, and a commentary category. Eachcategory-score in FIG. 4B may be based on weighted sub-scorescorresponding to the example weighted sub-scores for recency,social-relevance, text similarity, and author quality. As an example andnot by way of limitation, the category-score corresponding to the newscategory bay be based on the weighted sub-scores of 0.2, 0.4, 0.1, and0.3, corresponding to the recency, social relevance, text similarity,and author quality axes, respectively. Although FIG. 4B illustratesparticular categories, this disclosure contemplates any suitablecategories.

In particular embodiments, each category-score for an identified objectmay be calculated based on a weighting of sub-scores for the identifiedobject. As an example and not by way of limitation, the identifiedobject for FIG. 4B may have a sub-score R (author quality)=0.6corresponding to the author quality axis. In this example, a weightingof a(author quality, news)=a(author quality, celebrity)=0.5 may be usedfor the author quality axis with respect to the news category andcelebrity category, a weight of a(author quality, how to)=1.0 may beused for the author quality axis with respect to the how-to axis, and aweight of a(author quality, commentary)=⅙ may be used for the authorquality axis with respect to the commentary axis. The weighted sub-scorewith respect to a category may be calculated by multiplying thesub-score by the weight, as illustrated by the entries in the table ofFIG. 4B, for which weighted sub-scores R(author quality)a(authorquality, news)=0.3, R(author quality)a(author a(author quality,celebrity)=0.3, R(author quality)a(author quality, how to)=0.6, andR(author quality)a(author a(author quality, commentary)=0.1. Althoughthis example uses particular values for sub-scores and weights, thisdisclosure contemplates that sub-scores and weights may have anysuitable value.

In particular embodiments, the weighting of the plurality of sub-scoresfor a category-score may be a weighted arithmetic mean of the pluralityof sub-scores. As an example and not by way of limitation, the newscategory in FIG. 4B may use weights of 0.9, 0.3, 0.3, and 0.5 for therecency, social relevance, text similarity, and author quality axes,respectively. A category-score for the news category may be calculatedas the sum of weighted sub-scores divided by the sum of weights. In thisexample, the category-score for the news category may be0.2+0.4+0.1+0.3/0.9+0.3+0.3+0.5=0.5. Although this disclosure may useparticular sub-scores, weights, and category-scores, this disclosurecontemplates any suitable sub-scores, weights, and category-scores.

In particular embodiments, each category-score may be calculated basedon a degree of separation between the first node and a second nodecorresponding to the identified object. As an example and not by way oflimitation, a user may have liked an entity related to business andfinance news, such as the WALL STREET JOURNAL. Based on this, anidentified object associated with Jeff Bezos, founder and CEO of AMAZON,may receive a higher category-score with respect to a celebrity categorythan for a user who has no connection to business and finance news.Although this disclosure may describe calculating a category-score basedon a degree of separation in a particular manner, this disclosurecontemplates calculating a category-score based on a degree ofseparation in any suitable manner.

FIG. 5A illustrates an example set of category scores for an example setof identified objects. In particular embodiments, the social-networkingsystem 160 may calculate, for each identified object, a plurality ofcategory-scores corresponding to a plurality of categories,respectively. In this example, each of object_1 to object_N maycorrespond to an identified object and each of category_1 to cateogry_Cmay correspond to a category. Further, S_(n)(c) may be thecategory-score of identified object n with respect to category c.

In particular embodiments, each category-score S for an identifiedobject n may be calculated based on a weighting of sub-scores for theidentified object. In particular embodiments, the weighting of theplurality of sub-scores for a category-score may be a weightedarithmetic mean of the plurality of sub-scores. As an example and not byway of limitation, the category-score S for identified object n withrespect to category c may be calculated as

${S_{n}(c)} = \frac{\sum_{k}{{a\left( {k,c} \right)}{R(k)}}}{\sum_{k}{a\left( {k,c} \right)}}$

where R(k) may be the sub-score of identified object n with respect tothe k^(th) axis, and a(k, c) may be a weight of the k^(th) sub-scorewith respect to category c.

In particular embodiments, the plurality of categories may comprise oneor more pre-determined categories. Pre-determined categories may bedetermined prior identifying a set of objects in response to a query. Asan example and not by way of limitation, pre-determined categories mayinclude a domain expert category, a celebrity category, a reviewcategory, a how-to category, a commentary category, a news category, orany other suitable category. Although this disclosure describesparticular pre-determined categories, this disclosure contemplates useof any suitable pre-determined categories.

In particular embodiments, the plurality of categories may comprise oneor more categories determined dynamically based on one or more topicsassociated with the identified objects. As an example and not by way oflimitation, the social-networking system 160 may receive a search queryfor “THE AVENGERS.” In this example, dynamic categories may include“IRON MAN” or “CAPTAIN AMERICA” based on these topics appearing in manyof the identified objects. In other words, dynamic categoriescorresponding to particular topics are created based on the appearanceof those topics in the identified objects. The social-networking system160 may calculate a category-score for the one or more dynamicallydetermined categories. As an example and not by way of limitation, if“IRON MAN” is a dynamically determined category, a category-score forthe “IRON MAN” category may be calculated. In particular embodiments, atopic of an identified object and a corresponding dynamic category maybe determined by a topic tagger. The social-networking system 160 mayutilize a topic tagger to identify topics associated with identifiedobjects, as disclosed in U.S. patent application Ser. No. 14/470583,filed 27 Aug. 2014, which is incorporated herein by reference. As anexample and not by way of limitation, a topic tagger may determine thata number of identified objects are related to the topic “CAPTAINAMERICA,” and based on this, “CAPTAIN AMERICA” may be dynamicallydetermined as a category. In particular embodiments, the plurality ofcategories may comprise one or more categories determined dynamicallybased on a language-model analysis of the identified objects. As anexample and not by way of limitation, using the example search query for“THE AVENGERS,” dynamic categories may include “IRON MAN” or “CAPTAINAMERICA” based on a language model (e.g., the n-grams “IRON MAN,”“CAPTAIN AMERICA,” or “TONY STARK” appear with a relatively highfrequency in the results). Although this disclosure describesdynamically determining categories in a particular manner, thisdisclosure contemplates dynamically determining categories in anysuitable manner.

FIG. 5B illustrates an example set of particular category-scorescorresponding to an example set of identified objects. In particularembodiments, the social-networking system 160 may categorize eachidentified object into a category of the plurality of categories basedon the category-scores for the identified object. As an example and notby way of limitation, referencing FIG. 5B, object_6 may be categorizedinto the celebrity category based having a category-score of 0.7 withrespect to the celebrity category, compared to the lower category-scoreswith respect to other categories. In particular embodiments, eachidentified object may be categorized into no more than one category ofthe plurality of categories. An identified object may be categorizedinto only the category for which it has the highest category-score. Forexample, object_4 may be categorized into the how-to category based on acategory-score of 0.8 with respect to the how-to category, but not thecelebrity category, despite receiving a 0.7 category-score with respectto the celebrity category. Although FIG. 5B illustrates particularcategories, objects, and category-scores, this disclosure contemplatesany suitable categories, objects, or category-scores.

In particular embodiments, categorizing each identified object into acategory of the plurality of categories may be based on a pre-filteringprocess. As an example and not by way of limitation, as discussed above,the social-networking system 160 may utilize a topic tagger to identifytopics associated with identified objects. A topic tagger may associatean identified object with a particular topic. An identified object maybe categorized based on topics associated with the object. As an exampleand not by way of limitation, a topic tagger may identify that aparticular identified object is associated with the topic of celebritysinger-songwriter Taylor Swift. Further, in this example, there mayexist a celebrity category. Based on the association of the particularidentified object with a celebrity, that object may be categorized intothe celebrity category. In particular embodiments, an identified objectmay comprise metadata associated with a particular category. As anexample and not by way of limitation, an identified object may comprisemetadata indicating that it is a news article (e.g., the objectcomprises metadata indicated that it is an article associated with thewebsite CNN.com, a news content provider). In this example, theidentified object may be categorized in a news category based on themetadata. Although this disclosure describes categorizing an identifiedobject into a category based on a pre-filtering process in a particularmanner, this disclosure contemplates categorizing an identified objectinto a category based on a pre-filtering process in any suitable manner.

In particular embodiments, categorizing each identified object into acategory of the plurality of categories may be based on anatural-language model analysis of the identified object. In particularembodiments, a natural-language model may utilize a deep-learning model(e.g., a machine learning model, a neural network, etc.). Anatural-language model may be a unigram model, an n-gram model, acontinuous space language model, or any other suitable language model.As an example and not by way of limitation, an analysis of a particularidentified object based on natural-language model may determine thatthere is a 94% probability that the object is associated with news. Inthis example, the particular identified object may contain n-grams thatindicate that the object is a news article (e.g., the object mayreference a common news topic or contain terminology associated withnews articles). Based on this natural-language analysis of the object,the object may be categorized into a news category. Although thisdisclosure describes categorizing an identified object into a categorybased on a natural-language model analysis in a particular manner, thisdisclosure contemplates categorizing an identified object into acategory based on a natural-language model analysis in any suitablemanner.

In particular embodiments, the social-networking system 160 may adjustthe category-scores for one or more of the identified objects based onan author diversity among the categorized objects having the highestcalculated category-scores. As an example and not by way of limitation,a particular set of identified objects may comprise a large number ofidentified objects with the highest category-scores authored bycolumnist Ezra Klein. In this example, some of the objects authored byEzra Klein may have the category-score adjusted downward, while otherobjects authored by Ezra Klein may retain the original category-score.This may be promote search results that feature a diverse set ofauthors. In particular embodiments, an author may be a person or aninstitution. As an example and not by way of limitation, for an objectcomprising an article authored by Roger Cohen for the NEW YORK TIMES,the author may be Roger Cohen or the NEW YORK TIMES. Although thisdisclosure describes adjusting category-scores based on author diversityin a particular manner, this disclosure contemplates adjustingcategory-scores based on author diversity in any suitable manner.

In particular embodiments, the social-networking system 160 may send, tothe client system 130 in response to the query, one or more searchresults corresponding to one or more of the categorized objects fordisplay. Each search result may reference the respective categorizedobject. The one or more categorized objects of the search results maycomprises objects categorized into one or more selected categories. Foreach selected category one of the search results may correspond to acategorized object having a highest calculated category-scorecorresponding to the respective selected category. The search resultsmay be sent to the user, for example, in the form of a list of links ona search-results webpage, each link being associated with a differentwebpage that contains some of the identified resources or content. Inparticular embodiments, each link in the search results may be in theform of a Uniform Resource Locator (URL) that specifies where thecorresponding webpage is located and the mechanism for retrieving it.The social-networking system 160 may then send the search-resultswebpage to the web browser 132 on the user's client system 130. The usermay then click on the URL links or otherwise select the content from thesearch-results webpage to access the content from the social-networkingsystem 160 or from an external system (such as, for example, third-partysystem 170), as appropriate. In particular embodiments, each searchresult may include a link to a profile interface and a description orsummary of the profile interface (or the node corresponding to thatprofile interface). The search results may be presented and sent to thequerying user as a search-results interface. The search-resultsinterface may display search results based on the categories of therespective identified objects. As an example and not by way oflimitation, a search-results interface may display separate lists foreach selected category, each list comprising the search results with thehighest category-scores for the corresponding selected categories. Whengenerating the search results, the social-networking system 160 maygenerate and send to the querying user one or more snippets for eachsearch result, where the snippets are contextual information about thetarget of the search result (i.e., contextual information about thesocial-graph entity, profile interface, or other content correspondingto the particular search result). In particular embodiments, categoriesmay be selected based on a pre-determined selection of categories. As anexample and not by way of limitation, a news category may be apre-determined selected category. Additionally or alternatively,categories may be selected based on the identified objects. As anexample and not by way of limitation, categories may be selected basedon a number of identified objects being categorized into a category,having a threshold category-score, or based on identified objects in anysuitable manner. As an example and not by way of limitation, if at least10 percent of the identified objects are categorized into a particularcategory, that particular category may be selected. As another exampleand not by way of limitation, if a threshold number of identifiedobjects are categorized into a particular category, and at least 15percent of those objects have at least threshold category-scorecorresponding to the particular category, then that particular may beselected. In particular embodiments, categories may be selected based ona degree of separation between the first node and a one or more secondnodes associated with one or more of the selected categories. Forexample, a news category may be selected based on a relatively smalldegree of separation between the node corresponding to the user and anode corresponding to a news category. Although this disclosuredescribes sending particular search results and selecting categories ina particular manner, this disclosure contemplates sending any suitablesearch results and selecting categories in any suitable manner.

In particular embodiments, for each selected category, the one or moresearch results categorized into the selected category may comprise a setof blended search results. The blended search results may be generatedby blending the plurality of sub-sets of identified objects from theplurality of verticals. As discussed above, identifying the set ofobjects associated with the online social network matching the query maycomprise searching a plurality of verticals to identify a plurality ofsub-sets of objects from the plurality of verticals, respectively, thatmatch the search query. Blending search results may refer to a processwhere a plurality of sets of identified objects are combined, orblended, to form a set of blended search results that may be returned inresponse to a search query. Each sub-set may be associated with aparticular vertical. In connection with blended search results,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 14/454826, filed 8 Aug. 2016 and U.S. patentapplication Ser. No. 14/244748, filed 3 Apr. 2014, each of which areincorporated by reference. Although this disclosure describes blendingsearch results in a particular manner, this disclosure contemplatesblending search results in any suitable manner.

In particular embodiments, the social-networking system 160 may receive,from the client system 130, a request for additional search resultsassociated with a particular category of the plurality of categories. Asan example and not by way of limitation, a search-results interface maydisplay search results organized by the selected categories, with aseparate list of search results corresponding to each selected categorybeing displayed. In this example, each category may have a button orlink that allows a user to search for more search results within aparticular category. If a user clicks the button, the client system 130may send to the social-networking system 160 a request for additionalsearch results associated with the particular category. In particularembodiments, the social-networking system 160 may send, to the clientsystem 130 in response to the request, one or more search resultscorresponding to the particular category. As an example and not by wayof limitation, if the request is for more search results correspondingto a commentary category, the social-networking system 160 may identifyand send additional search results corresponding to the commentarycategory. Although this disclosure describes sending additional searchresults associated with a particular category in respond to a request ina particular manner, this disclosure contemplates sending additionalsearch results associated with a particular category in respond to arequest in any suitable manner.

In particular embodiments, the social-networking system 160 may, foreach selected category, rank the search results corresponding to theselected category based on the category-score for the categorized objectcorresponding to the search result. As an example and not by way oflimitation, search results corresponding to objects with highercategory-scores may be ranked higher. In particular embodiments, higherranked objects may be displayed above lower ranked objects. Althoughthis disclosure describes ranking search based on a category-score in aparticular manner, this disclosure contemplates ranking search based ona category-score in any suitable manner.

FIG. 6 illustrates an example post on a social network and an examplegraphical representation of category-scores for the post. Post 610 maybe a post from television personality and chef Alton Brown, whichincludes text and a link to a recipe for watermelon rind pickles. Thispost may match a query received from a user, for example, a query for“pickle recipes.” Graph 620 may be a graphical representation ofcategory-scores for post 610 with respect to a celebrity category, acommentary category, a how-to category, a news category, and a reviewcategory. For each of these categories, the corresponding category-scorecalculated for post 610 may be plotted along the corresponding axes.Graph 620 may allow a user to quickly ascertain relative category-scoresfor post 610. For example, graph 620 shows that post 610 has relativelyhigh category-scores for the celebrity and how-to categories, with thehighest category-score corresponding to the how-to category. AlthoughFIG. 6 illustrates a particular post and a particular graphicalrepresentation of category-scores, this disclosure contemplates anysuitable post and any suitable graphical representation of any suitablecategory-scores.

FIG. 7 illustrates an example method 700 for categorizing objects basedon a plurality of category-scores. The method may begin at step 710,where the social-networking system 160 may receive, from a client systemof a user of an online social network, a query inputted by the user. Atstep 720, the social-networking system 160 may identify a set of objectsassociated with the online social network matching the query. At step730, the social-networking system 160 may calculate, for each identifiedobject, a plurality of category-scores corresponding to a plurality ofcategories, respectively, wherein each category-score may be calculatedbased on a plurality of sub-scores corresponding to a plurality ofscoring axes, respectively, each scoring axis corresponding to aparticular factor associated with the respective scoring axis, andwherein each category-score may be calculated based on a weighting ofthe plurality of sub-scores, the weighting for each sub-score beingbased on the category corresponding to the respective category-score. Atstep 740, the social-networking system 160 may categorize eachidentified object into a category of the plurality of categories basedon the category-scores for the identified object. At step 750, thesocial-networking system 160 may send, to the client system 130 inresponse to the query, one or more search results corresponding to oneor more of the categorized objects for display, each search resultreferencing the respective categorized object, wherein the one or morecategorized objects of the search results comprises objects categorizedinto one or more selected categories, and wherein for each selectedcategory one of the search results may correspond to a categorizedobject having a highest calculated category-score corresponding to therespective selected category. Particular embodiments may repeat one ormore steps of the method of FIG. 7, where appropriate. Although thisdisclosure describes and illustrates particular steps of the method ofFIG. 7 as occurring in a particular order, this disclosure contemplatesany suitable steps of the method of FIG. 7 occurring in any suitableorder. Moreover, although this disclosure describes and illustrates anexample method for categorizing objects based on a plurality ofcategory-scores including the particular steps of the method of FIG. 7,this disclosure contemplates any suitable method for categorizingobjects based on a plurality of category-scores including any suitablesteps, which may include all, some, or none of the steps of the methodof FIG. 7, where appropriate. Furthermore, although this disclosuredescribes and illustrates particular components, devices, or systemscarrying out particular steps of the method of FIG. 7, this disclosurecontemplates any suitable combination of any suitable components,devices, or systems carrying out any suitable steps of the method ofFIG. 7.

FIG. 8 illustrates an example method 800 for categorizing objects basedon a plurality of category-scores using a pre-filtering process. Themethod may begin at step 810, where the social-networking system 160 mayreceive, from a client system 130 of a user of an online social network,a query inputted by the user. At step 820, the social-networking system160 may identify a set of objects associated with the online socialnetwork matching the query. At step 830, the social-networking system160 may determine, for each identified object, one or more topicsassociated with the identified object. At step 840, thesocial-networking system 160 may categorize each identified object intoa category of a plurality of categories based on the one or more topicsassociated with the identified object. At step 850, thesocial-networking system 160 may send, to the client system 130 inresponse to the query, one or more search results corresponding to oneor more of the categorized objects for display, each search resultreferencing the respective categorized object, wherein the one or morecategorized objects of the search results comprises objects categorizedinto one or more selected categories. Particular embodiments may repeatone or more steps of the method of FIG. 8, where appropriate. Althoughthis disclosure describes and illustrates particular steps of the methodof FIG. 8 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 8 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for categorizing objects based on aplurality of category-scores including the particular steps of themethod of FIG. 8, this disclosure contemplates any suitable method forcategorizing objects based on a plurality of category-scores includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 8, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 8, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 8.

Social Graph Affinity and Coefficient

In particular embodiments, the social-networking system 160 maydetermine the social-graph affinity (which may be referred to herein as“affinity”) of various social-graph entities for each other. Affinitymay represent the strength of a relationship or level of interestbetween particular objects associated with the online social network,such as users, concepts, content, actions, advertisements, other objectsassociated with the online social network, or any suitable combinationthereof. Affinity may also be determined with respect to objectsassociated with third-party systems 170 or other suitable systems. Anoverall affinity for a social-graph entity for each user, subjectmatter, or type of content may be established. The overall affinity maychange based on continued monitoring of the actions or relationshipsassociated with the social-graph entity. Although this disclosuredescribes determining particular affinities in a particular manner, thisdisclosure contemplates determining any suitable affinities in anysuitable manner.

In particular embodiments, the social-networking system 160 may measureor quantify social-graph affinity using an affinity coefficient (whichmay be referred to herein as “coefficient”). The coefficient mayrepresent or quantify the strength of a relationship between particularobjects associated with the online social network. The coefficient mayalso represent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated based at least in part on the history of the user'sactions. Coefficients may be used to predict any number of actions,which may be within or outside of the online social network. As anexample and not by way of limitation, these actions may include varioustypes of communications, such as sending messages, posting content, orcommenting on content; various types of observation actions, such asaccessing or viewing profile interfaces, media, or other suitablecontent; various types of coincidence information about two or moresocial-graph entities, such as being in the same group, tagged in thesame photograph, checked-in at the same location, or attending the sameevent; or other suitable actions. Although this disclosure describesmeasuring affinity in a particular manner, this disclosure contemplatesmeasuring affinity in any suitable manner.

In particular embodiments, the social-networking system 160 may use avariety of factors to calculate a coefficient. These factors mayinclude, for example, user actions, types of relationships betweenobjects, location information, other suitable factors, or anycombination thereof. In particular embodiments, different factors may beweighted differently when calculating the coefficient. The weights foreach factor may be static or the weights may change according to, forexample, the user, the type of relationship, the type of action, theuser's location, and so forth. Ratings for the factors may be combinedaccording to their weights to determine an overall coefficient for theuser. As an example and not by way of limitation, particular useractions may be assigned both a rating and a weight while a relationshipassociated with the particular user action is assigned a rating and acorrelating weight (e.g., so the weights total 100%). To calculate thecoefficient of a user towards a particular object, the rating assignedto the user's actions may comprise, for example, 60% of the overallcoefficient, while the relationship between the user and the object maycomprise 40% of the overall coefficient. In particular embodiments, thesocial-networking system 160 may consider a variety of variables whendetermining weights for various factors used to calculate a coefficient,such as, for example, the time since information was accessed, decayfactors, frequency of access, relationship to information orrelationship to the object about which information was accessed,relationship to social-graph entities connected to the object, short- orlong-term averages of user actions, user feedback, other suitablevariables, or any combination thereof. As an example and not by way oflimitation, a coefficient may include a decay factor that causes thestrength of the signal provided by particular actions to decay withtime, such that more recent actions are more relevant when calculatingthe coefficient. The ratings and weights may be continuously updatedbased on continued tracking of the actions upon which the coefficient isbased. Any type of process or algorithm may be employed for assigning,combining, averaging, and so forth the ratings for each factor and theweights assigned to the factors. In particular embodiments, thesocial-networking system 160 may determine coefficients usingmachine-learning algorithms trained on historical actions and past userresponses, or data farmed from users by exposing them to various optionsand measuring responses. Although this disclosure describes calculatingcoefficients in a particular manner, this disclosure contemplatescalculating coefficients in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on a user's actions. The social-networkingsystem 160 may monitor such actions on the online social network, on athird-party system 170, on other suitable systems, or any combinationthereof. Any suitable type of user actions may be tracked or monitored.Typical user actions include viewing profile interfaces, creating orposting content, interacting with content, tagging or being tagged inimages, joining groups, listing and confirming attendance at events,checking-in at locations, liking particular interfaces, creatinginterfaces, and performing other tasks that facilitate social action. Inparticular embodiments, the social-networking system 160 may calculate acoefficient based on the user's actions with particular types ofcontent. The content may be associated with the online social network, athird-party system 170, or another suitable system. The content mayinclude users, profile interfaces, posts, news stories, headlines,instant messages, chat room conversations, emails, advertisements,pictures, video, music, other suitable objects, or any combinationthereof. The social-networking system 160 may analyze a user's actionsto determine whether one or more of the actions indicate an affinity forsubject matter, content, other users, and so forth. As an example andnot by way of limitation, if a user frequently posts content related to“coffee” or variants thereof, the social-networking system 160 maydetermine the user has a high coefficient with respect to the concept“coffee”. Particular actions or types of actions may be assigned ahigher weight and/or rating than other actions, which may affect theoverall calculated coefficient. As an example and not by way oflimitation, if a first user emails a second user, the weight or therating for the action may be higher than if the first user simply viewsthe user-profile interface for the second user.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on the type of relationship betweenparticular objects. Referencing the social graph 200, thesocial-networking system 160 may analyze the number and/or type of edges206 connecting particular user nodes 202 and concept nodes 204 whencalculating a coefficient. As an example and not by way of limitation,user nodes 202 that are connected by a spouse-type edge (representingthat the two users are married) may be assigned a higher coefficientthan a user nodes 202 that are connected by a friend-type edge. In otherwords, depending upon the weights assigned to the actions andrelationships for the particular user, the overall affinity may bedetermined to be higher for content about the user's spouse than forcontent about the user's friend. In particular embodiments, therelationships a user has with another object may affect the weightsand/or the ratings of the user's actions with respect to calculating thecoefficient for that object. As an example and not by way of limitation,if a user is tagged in a first photo, but merely likes a second photo,the social-networking system 160 may determine that the user has ahigher coefficient with respect to the first photo than the second photobecause having a tagged-in-type relationship with content may beassigned a higher weight and/or rating than having a like-typerelationship with content. In particular embodiments, thesocial-networking system 160 may calculate a coefficient for a firstuser based on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, the social-networking system 160may determine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph200. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 200 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 200.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on location information. Objects that aregeographically closer to each other may be considered to be more relatedor of more interest to each other than more distant objects. Inparticular embodiments, the coefficient of a user towards a particularobject may be based on the proximity of the object's location to acurrent location associated with the user (or the location of a clientsystem 130 of the user). A first user may be more interested in otherusers or concepts that are closer to the first user. As an example andnot by way of limitation, if a user is one mile from an airport and twomiles from a gas station, the social-networking system 160 may determinethat the user has a higher coefficient for the airport than the gasstation based on the proximity of the airport to the user.

In particular embodiments, the social-networking system 160 may performparticular actions with respect to a user based on coefficientinformation. Coefficients may be used to predict whether a user willperform a particular action based on the user's interest in the action.A coefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, the social-networking system 160 may provideinformation that is relevant to user's interests and currentcircumstances, increasing the likelihood that they will find suchinformation of interest. In particular embodiments, thesocial-networking system 160 may generate content based on coefficientinformation. Content objects may be provided or selected based oncoefficients specific to a user. As an example and not by way oflimitation, the coefficient may be used to generate media for the user,where the user may be presented with media for which the user has a highoverall coefficient with respect to the media object. As another exampleand not by way of limitation, the coefficient may be used to generateadvertisements for the user, where the user may be presented withadvertisements for which the user has a high overall coefficient withrespect to the advertised object. In particular embodiments, thesocial-networking system 160 may generate search results based oncoefficient information. Search results for a particular user may bescored or ranked based on the coefficient associated with the searchresults with respect to the querying user. As an example and not by wayof limitation, search results corresponding to objects with highercoefficients may be ranked higher on a search-results interface thanresults corresponding to objects having lower coefficients.

In particular embodiments, the social-networking system 160 maycalculate a coefficient in response to a request for a coefficient froma particular system or process. To predict the likely actions a user maytake (or may be the subject of) in a given situation, any process mayrequest a calculated coefficient for a user. The request may alsoinclude a set of weights to use for various factors used to calculatethe coefficient. This request may come from a process running on theonline social network, from a third-party system 170 (e.g., via an APIor other communication channel), or from another suitable system. Inresponse to the request, the social-networking system 160 may calculatethe coefficient (or access the coefficient information if it haspreviously been calculated and stored). In particular embodiments, thesocial-networking system 160 may measure an affinity with respect to aparticular process. Different processes (both internal and external tothe online social network) may request a coefficient for a particularobject or set of objects. The social-networking system 160 may provide ameasure of affinity that is relevant to the particular process thatrequested the measure of affinity. In this way, each process receives ameasure of affinity that is tailored for the different context in whichthe process will use the measure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632869, filed 1 Oct. 2012, each of which isincorporated by reference.

Advertising

In particular embodiments, an advertisement may be text (which may beHTML-linked), one or more images (which may be HTML-linked), one or morevideos, audio, one or more ADOBE FLASH files, a suitable combination ofthese, or any other suitable advertisement in any suitable digitalformat presented on one or more web interfaces, in one or more e-mails,or in connection with search results requested by a user. In addition oras an alternative, an advertisement may be one or more sponsored stories(e.g., a news-feed or ticker item on the social-networking system 160).A sponsored story may be a social action by a user (such as “liking” aninterface, “liking” or commenting on a post on an interface, RSVPing toan event associated with an interface, voting on a question posted on aninterface, checking in to a place, using an application or playing agame, or “liking” or sharing a website) that an advertiser promotes, forexample, by having the social action presented within a pre-determinedarea of a profile interface of a user or other interface, presented withadditional information associated with the advertiser, bumped up orotherwise highlighted within news feeds or tickers of other users, orotherwise promoted. The advertiser may pay to have the social actionpromoted. As an example and not by way of limitation, advertisements maybe included among the search results of a search-results interface,where sponsored content is promoted over non-sponsored content.

In particular embodiments, an advertisement may be requested for displaywithin social-networking-system web interfaces, third-party webinterfaces, or other interfaces. An advertisement may be displayed in adedicated portion of an interface, such as in a banner area at the topof the interface, in a column at the side of the interface, in a GUIwithin the interface, in a pop-up window, in a drop-down menu, in aninput field of the interface, over the top of content of the interface,or elsewhere with respect to the interface. In addition or as analternative, an advertisement may be displayed within an application. Anadvertisement may be displayed within dedicated interfaces, requiringthe user to interact with or watch the advertisement before the user mayaccess an interface or utilize an application. The user may, for exampleview the advertisement through a web browser.

A user may interact with an advertisement in any suitable manner. Theuser may click or otherwise select the advertisement. By selecting theadvertisement, the user may be directed to (or a browser or otherapplication being used by the user) an interface associated with theadvertisement. At the interface associated with the advertisement, theuser may take additional actions, such as purchasing a product orservice associated with the advertisement, receiving informationassociated with the advertisement, or subscribing to a newsletterassociated with the advertisement. An advertisement with audio or videomay be played by selecting a component of the advertisement (like a“play button”). Alternatively, by selecting the advertisement, thesocial-networking system 160 may execute or modify a particular actionof the user.

An advertisement may also include social-networking-system functionalitythat a user may interact with. As an example and not by way oflimitation, an advertisement may enable a user to “like” or otherwiseendorse the advertisement by selecting an icon or link associated withendorsement. As another example and not by way of limitation, anadvertisement may enable a user to search (e.g., by executing a query)for content related to the advertiser. Similarly, a user may share theadvertisement with another user (e.g., through the social-networkingsystem 160) or RSVP (e.g., through the social-networking system 160) toan event associated with the advertisement. In addition or as analternative, an advertisement may include social-networking-systemcontent directed to the user. As an example and not by way oflimitation, an advertisement may display information about a friend ofthe user within the social-networking system 160 who has taken an actionassociated with the subject matter of the advertisement.

Systems and Methods

FIG. 9 illustrates an example computer system 900. In particularembodiments, one or more computer systems 900 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 900 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 900 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 900.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems900. This disclosure contemplates computer system 900 taking anysuitable physical form. As example and not by way of limitation,computer system 900 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, or acombination of two or more of these. Where appropriate, computer system900 may include one or more computer systems 900; be unitary ordistributed; span multiple locations; span multiple machines; spanmultiple data centers; or reside in a cloud, which may include one ormore cloud components in one or more networks. Where appropriate, one ormore computer systems 900 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 900 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 900 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 900 includes a processor 902,memory 904, storage 906, an input/output (I/O) interface 908, acommunication interface 910, and a bus 912. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 902 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 902 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 904, or storage 906; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 904, or storage 906. In particular embodiments, processor902 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 902 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 902 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 904 or storage 906, andthe instruction caches may speed up retrieval of those instructions byprocessor 902. Data in the data caches may be copies of data in memory904 or storage 906 for instructions executing at processor 902 tooperate on; the results of previous instructions executed at processor902 for access by subsequent instructions executing at processor 902 orfor writing to memory 904 or storage 906; or other suitable data. Thedata caches may speed up read or write operations by processor 902. TheTLBs may speed up virtual-address translation for processor 902. Inparticular embodiments, processor 902 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 902 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 902may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 902. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 904 includes main memory for storinginstructions for processor 902 to execute or data for processor 902 tooperate on. As an example and not by way of limitation, computer system900 may load instructions from storage 906 or another source (such as,for example, another computer system 900) to memory 904. Processor 902may then load the instructions from memory 904 to an internal registeror internal cache. To execute the instructions, processor 902 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 902 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor902 may then write one or more of those results to memory 904. Inparticular embodiments, processor 902 executes only instructions in oneor more internal registers or internal caches or in memory 904 (asopposed to storage 906 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 904 (as opposedto storage 906 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 902 tomemory 904. Bus 912 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 902 and memory 904 and facilitateaccesses to memory 904 requested by processor 902. In particularembodiments, memory 904 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 904 may include one ormore memories 904, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 906 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 906may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage906 may include removable or non-removable (or fixed) media, whereappropriate. Storage 906 may be internal or external to computer system900, where appropriate. In particular embodiments, storage 906 isnon-volatile, solid-state memory. In particular embodiments, storage 906includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 906 taking any suitable physicalform. Storage 906 may include one or more storage control unitsfacilitating communication between processor 902 and storage 906, whereappropriate. Where appropriate, storage 906 may include one or morestorages 906. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 908 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 900 and one or more I/O devices. Computer system900 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 900. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 908 for them. Where appropriate, I/O interface 908 mayinclude one or more device or software drivers enabling processor 902 todrive one or more of these I/O devices. I/O interface 908 may includeone or more I/O interfaces 908, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 910 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 900 and one or more other computer systems 900 or one ormore networks. As an example and not by way of limitation, communicationinterface 910 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 910 for it. As an example and not by way of limitation,computer system 900 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 900 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 900 may include any suitable communication interface 910 for anyof these networks, where appropriate. Communication interface 910 mayinclude one or more communication interfaces 910, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 912 includes hardware, software, or bothcoupling components of computer system 900 to each other. As an exampleand not by way of limitation, bus 912 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 912may include one or more buses 912, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computingsystems: receiving, by the one or more computing systems, from a clientsystem of a user, a query inputted by the user; identifying, by the oneor more computing systems, a set of objects matching the query, whereineach identified object is associated with one or more topics;categorizing, by the one or more computing systems, each identifiedobject into a category of the plurality of categories based on the oneor more topics associated with the identified object; and sending, bythe one or more computing systems, to the client system in response tothe query, one or more search results corresponding to one or more ofthe categorized objects for display, each search result referencing therespective categorized object, wherein the one or more categorizedobjects of the search results comprises objects categorized into one ormore selected categories.
 2. The method of claim 1, wherein a topictagger is utilized to identify the one or more topics associated withthe each identified object.
 3. The method of claim 1, wherein theplurality of categories comprises one or more of a news category, acelebrity category, a commentary category, a domain expert category, areview category, or a how-to category.
 4. The method of claim 1, whereinthe plurality of categories comprises one or more categories determineddynamically based on one or more topics associated with the identifiedobjects.
 5. The method of claim 1, wherein the plurality of categoriescomprises one or more categories determined dynamically based on alanguage-model analysis of the identified objects.
 6. The method ofclaim 1, wherein the plurality of categories comprises one or morepre-determined categories.
 7. The method of claim 1, whereincategorizing each identified object into a category of the plurality ofcategories is further based on a natural-language model analysis of theidentified object.
 8. The method of claim 1, wherein categorizing eachidentified object into a category of the plurality of categories isfurther based on a plurality of category-scores associated with theidentified object.
 9. The method of claim 8, wherein the plurality ofcategory-scores corresponding to the plurality of categories,respectively, and wherein each category-score is calculated based on aplurality of sub-scores corresponding to a plurality of scoring axes,respectively, each scoring axis corresponding to a particular factorassociated with the respective scoring axis, and wherein eachcategory-score is calculated based on a weighting of the plurality ofsub-scores, the weighting for each sub-score being based on the categorycorresponding to the respective category-score.
 10. The method of claim9, wherein the weighting of the plurality of sub-scores for at least onecategory-score comprises a weighted arithmetic mean of the plurality ofsub-scores.
 11. The method of claim 9, further comprising adjusting thecategory-scores for one or more of the identified objects based on anauthor diversity among the categorized objects having the highestcalculated category-scores.
 12. The method of claim 1, whereinidentifying the set of objects matching the query comprises searching aplurality of verticals to identify a plurality of sub-sets of objectsfrom the plurality of verticals, respectively, that match the searchquery.
 13. The method of claim 12, wherein, for each selected category,the one or more search results categorized into the selected categorycomprise a set of blended search results, wherein the blended searchresults are generated by blending the plurality of sub-sets ofidentified objects from the plurality of verticals.
 14. The method ofclaim 1, further comprising receiving, from the client system, a requestfor additional search results associated with a particular category ofthe plurality of categories; and sending, to the client system inresponse to the request, one or more search results corresponding to theparticular category.
 15. The method of claim 1, further comprising:accessing a social graph comprising a plurality of nodes and a pluralityof edges connecting the nodes, each of the edges between two of thenodes representing a single degree of separation between them, the nodescomprising: a first node corresponding to the user; and a plurality ofsecond nodes corresponding to a plurality of objects associated with theonline social network, respectively.
 16. The method of claim 15, whereinthe one or more selected categories are selected based on a degree ofseparation between the first node and a one or more second nodesassociated with one or more of the selected categories.
 17. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to: receive from a client system of a user, aquery inputted by the user; identify a set of objects matching thequery, wherein each identified object is associated with one or moretopics; categorize each identified object into a category of theplurality of categories based on the one or more topics associated withthe identified object; and send to the client system in response to thequery, one or more search results corresponding to one or more of thecategorized objects for display, each search result referencing therespective categorized object, wherein the one or more categorizedobjects of the search results comprises objects categorized into one ormore selected categories.
 18. A system comprising: one or moreprocessors; and a non-transitory memory coupled to the processorscomprising instructions executable by the processors, the processorsoperable when executing the instructions to: receive from a clientsystem of a user, a query inputted by the user; identify a set ofobjects matching the query, wherein each identified object is associatedwith one or more topics; categorize each identified object into acategory of the plurality of categories based on the one or more topicsassociated with the identified object; and send to the client system inresponse to the query, one or more search results corresponding to oneor more of the categorized objects for display, each search resultreferencing the respective categorized object, wherein the one or morecategorized objects of the search results comprises objects categorizedinto one or more selected categories.